THEMIS.COG: Theoretical and Empirical Modeling of Identity and Sentiments in Collaborative Groups

这项拟议的研究将为自组织合作的动态提供新的见解，在这种合作中，人们有机地聚集在一起，解决共同的问题，而不需要第三方的提示。在当今社会，理解自我组织协作背后的社会力量变得越来越重要。技术和社会创新越来越多地通过非正式的分布式协作过程产生，而不是在正式的层级组织中产生。该项目将使用数据驱动的方法来探索激励自组织协作的社会和心理机制，并确定其成功或失败的可能性，重点是在线协作网络中的开放、协作软件开发的例子，如GitHub(githorb.com)。我们提供了一个精确的数学模型，用于预测和测试协作动态，该模型建立在社会学理论和研究的悠久传统之上。通过解释社会事件处理过程中的噪音和不确定性，这项工作将使研究社会心理机制对面对面交流以外的互动形式的影响以及跨文化互动成为可能。在面对面交流中，事件的解释远没有那么确定。这项研究建立在一个强大的群体过程社会学理论--情感控制理论(ACT)及其最近的概率推广--贝叶斯情感控制理论(BayesACT)的基础上。BayesACT应用贝叶斯概率理论的见解来解释人们是如何通过社会经验来学习和调整意义的，并展示了稳定的互动动态是如何从个人对自己和他人身份的不确定和嘈杂的感知中出现的。该模型基于这样一种观点，即人类在社交中的动机是情感一致：他们努力使自己的社会经历在深层、情感层面上与他们的认同感和他们与他人共享的文化信仰保持一致。BayesACT基于这样一个概念，即每个组成员拥有一个可学习的、数学上可描述的身份，并与其他组成员的身份互补，从而对协作组中的在线交互做出明确的预测。这些预测允许更深入、更有重点的数据挖掘，从而能够识别特定类型的交互，以便回答有关在线协作网络中的协作本质的问题。通过该项目产生的知识将在网上以及通过专业介绍和出版物广泛传播。此外，该项目将为一名博士后研究员和一批不同的本科生研究助理提供培训和指导。这一奖项是挖掘数据挑战第四轮的一部分，这是一个国际资助机会，旨在促进跨国研究合作，并鼓励以创新的方法分析社会科学和人文科学中的大数据集。美国的研究人员将与加拿大和德国的学者合作，以实现该项目的目标。

The proposed research will provide new insights into the dynamics of self-organized collaborations, in which people come together organically to work on a common problem, without prompting by a third party. Understanding the social forces behind self-organized collaboration is increasingly important in today's society. Technological and social innovations are increasingly generated through informal, distributed collaboration processes, rather than in formal, hierarchical organizations. This project will use a data-driven approach to explore the social and psychological mechanisms that motivate self-organized collaborations and determine their likelihood of success or failure, focusing on the example of open, collaborative software development in online collaborative networks like GitHub (github.com). We offer a mathematically precise model for predicting and testing collaborative dynamics, which builds on a long tradition of sociological theory and research. By accounting for noise and uncertainty in processing social events, this work will make it possible to study the reach of social psychological mechanisms to forms of interaction other than face-to-face communication, where interpretations of events are far less certain, and to cross-cultural interactions.The research builds on a powerful sociological theory of group processes known as affect control theory (ACT) and a recent probabilistic generalization of it, Bayesian affect control theory (BayesACT). BayesACT applies insights from Bayesian probability theory to explain how people learn and adjust meanings through social experience, and show how stable interaction dynamics emerge from individuals' uncertain and noisy perceptions of their own and others' identities. The model rests on the idea that humans are motivated in their social interactions by affective alignment: They strive for their social experiences to be coherent at a deep, emotional level with their sense of identity and the cultural beliefs they share with others. BayesACT makes explicit predictions about online interactions in a collaborative group, based on the notion that each group member holds an identity that is learnable, mathematically describable, and complementary to those of other group members. These predictions allow for deeper and more focused data mining, enabling the identification of interactions of specific types in order to answer questions about the very nature of collaboration within online collaborative networks. The knowledge generated through this project will be widely disseminated online, and through professional presentations and publications. In addition, the project will provide training and mentoring for a postdoctoral fellow and a diverse group of undergraduate research assistants. It will also contribute to education in the classroom.This award was made as part of Round 4 of the Digging Into Data Challenge, an international funding opportunity designed to foster research collaboration across countries and to encourage innovative approaches to analyzing large data sets in the social sciences and humanities. The U.S based researchers will collaborate with scholars in Canada and Germany to achieve the goals of this project.